1. Field of the Invention
The present invention relates to methods to create a user profile and to specify a suggestion for a next selection of a user, in particular to a method to create a user profile that comprises a list of word-weight pairs, a method to create an individual user profile from a multi-user profile that comprises a list of word-weight pairs, and a method to specify a suggestion for a next selection of a user, which suggestion is determined on basis of suggestion results, which are computed of future program descriptions and a user profile.
2. Discussion of the Background
Several technologies are known which make an attempt to make “reasonable suggestions” for all kinds of content selection applications to users of these applications. For such suggestion engines usually users and their preferences are modelled to present a most likely next content selection recommendation. One common way to model users and their preferences is to use predefined stereotypes of user groups that characterize the users interests concerning certain applications and/or topics. Also, the logging of user actions is widely used to infer certain characteristics therefrom. Such recommendation systems are often server-based and collect a lot of explicit ratings from a lot of users to build up profiles. Each user is then located in one of these learned profiles and under the assumption that the user will probably also like what the other users used to build up the profile liked, new suggestions of content selection probabilities will be made.
A model of a user and the corresponding preferences is usually a list of word-weight pairs, i.e. a list of weighted keywords, according to which new possible selections are searched to find the best matching one. The paper “Adaptivity through Unobstrusive Learning” by Ingo Schwab and Alfred Kopsa, Künstliche Intelligenz, Volume 3-02, pages 5-9, ISSN 0933-1875, Arendt Tapp Verlag, Bremen describes an approach for learning user profiles which consists of such a list of weighted keywords implicitly from positive user observations only. In this paper several methods together with user profiles are described. Further, in the paper “similarity measures for short queries” by Ross Wilkinson, Justin Zobel, Ron Sacks-Davis, Department of Computer Science, RMIT, GPO Box 2476V, Melbourne 3001, Australia, October 1995, Fourth Text Retrieval Conference (1995), 277-285, information retrieval techniques are described to find articles and databases based on certain keywords, in particular for ad-hoc queries which are usually short, of perhaps two to ten terms.
As mentioned above, the known systems compute suggestions based on an existing user profile and/or a users history, and it might be possible that the user profile can be edited directly by the user or it is computed automatically. As also mentioned above, another often used mechanism is to cluster groups of users according to their behaviour and/or selections, wherein an explicit rating about the contents from these users is necessary. It is then assumed that what the users in the group like will most likely also be a good suggestion for the current user. However, all these methods have in common that they make rather general suggestions, resulting from the overall history of the user or the group he belongs to. In the paper “Program driven music radio”, by Conor Hayes, Padraig Cunningham, Patrick Clerkin, Marco Grimaldi, Department of Computer Science, Trinity College Dublin, Proceedings of the ECAI 2002 (5th conference on artificial intelligence on 21. to 26. Jul. 2002), it is described to find better recommendations that are pertinent to a listener's current listening preference in streaming audio technology and recommendation techniques, according to which the resulting suggestions are filtered according to the current situation. The current situation here is the users behaviour in a certain (recent) time window to assure adaptation of the suggestions to an eventually changed taste of the user. However, also in this system the suggestion might be rather general to fit the current situation.
Such rather general suggestions that are valid for the user/user group as observed over a certain period of time might be inappropriate in certain situations.